The Sources & Power of Geographic Data
| Feature | Qualitative Data | Quantitative Data |
|---|---|---|
| Nature | Descriptive, subjective | Numerical, objective |
| Examples | Interviews, narratives | Population, income |
| Purpose | Understanding 'Why' and 'How' | Measuring 'How many' and 'Where' |
Scale of Analysis: The level of detail changes depending on whether data is viewed at a local, regional, or national scale. A national average can hide significant local disparities.
Granularity: High-resolution data (like census tracts) provides a detailed look at specific neighborhoods, whereas low-resolution data (like national literacy rates) provides a broad overview.
Identify the Source: When presented with a scenario, determine if the data came from a primary source (fieldwork) or a secondary source (census reports).
Check the Scale: Always ask if the data provided is at the correct scale to answer the question. If a question asks about neighborhood poverty, national-level data is likely insufficient.
Contextualize Census Data: Remember that the census is more than just a population count; it includes vital demographic markers like age, ethnicity, and gender that drive government policy.
Business Applications: Be prepared to explain how businesses use spatial data, such as using zip codes to analyze the purchasing power of potential customers in a specific radius.
Scale Masking: A common error is assuming that a national trend applies to every local area. For example, a country might have high overall wealth, but specific regions could be in extreme poverty.
Data Bias: Students often forget that qualitative data, like travel narratives, can be biased by the author's perspective and may not represent the objective reality of a place.
Correlation vs. Causation: Just because two spatial patterns overlap (e.g., high crime and high density) does not mean one causes the other without further qualitative investigation.